Driving visual information in highway tunnel entrances: A computational method based on optical flow and color quantification

作者：Yunwei Meng, Lei Wang, Yin Zhang⋆, Binbin Li, Zhongshuai Liu, Guangyan Qing, Chen Fang · 发表于：Traffic Injury Prevention · 年份：2025 · DOI：10.1080/15389588.2025.2498614 · 被引用次数：8 · 研究领域：Traffic and Road Safety、Human-Automation Interaction and Safety、Safety Warnings and Signage

OBJECTIVES: The environmental landscape of highway tunnel entrance zones is closely related to driving performance. To investigate the impact mechanism of environmental information volume on drivers' visual workload in tunnel entrance zones, this study proposes a novel computational method for quantifying visual information. The aim is to provide a theoretical basis for improving tunnel entrance environments and enhancing driving safety. METHODS: Field experiments on highways collected environmental images, vehicle dynamics, and drivers' speed and psychological data from eight tunnel entrances. Visual field images were divided into five regions based on attention range: upper portal, central portal, left/right roadside, and pavement. HSV values were extracted to describe color and texture features. A model combining optical flow, sight distance, lane width, and speed quantified visual information volume, including traffic signs, and analyzed its relationship with visual workload. RESULTS: The subjective questionnaire results were consistent with the objective computational findings, verifying the reliability of the proposed method. Tunnel entrances with complex landscapes and diverse traffic signs exhibited higher levels of visual information, with drivers' gaze distributed across four areas: both sides of the road, the tunnel entrance center, and the roadway. In contrast, entrances with simpler landscapes and fewer signs had lower visual information levels, and drivers' gaze...